Reliability and safety engineering springer series in. We developed two novel statistical models based on dempster shafer theory, which provide accurate predictions from relatively small data sets of direct and indirect software reliability and quality predictors. Knowledgesystem engineering electrical and computer. Most accurate prediction models require extensive testing effort, implying increased cost and slowing down the development life cycle. Dempster shafer theory does not require an assumption regarding the probability of the individual constituents of the set or interval. This book brings together a collection of classic research papers on the dempstershafer theory of belief functions. Dempstershafer theory based shipship collision probability. The application of this new decision theory is demonstrated through a water resources engineering design example. Bayesian theory is only concerned about single evidences.
Dempster in 1967 and his student glenn shafer in 1976. Thus, we believe that a distribution best captures a human opinion such as confidence. This course is an introduction to software engineering, and it covers the systematic development of software products. One major concern has been that information or knowledge, no matter how weak or sparse, must necessarily be represented by conventional, precisely specified. A verified matlab toolbox for the dempstershafer theory. Fundamentals in this section, we describe the fundamentals of the dempstershafer theory and interval methods brie. Department of electronic and information engineering, tongji university, shanghai, china correspondence ning jia, department of electronic and information engineering, no. Dempstershafer evidence theory approach to structural damage. Information fusion with dempstershafer evidence theory for software defect prediction aytunc paksoya, mehmet gokturka agebze institute of technology, istanbul street no. Apr 01, 2002 dempster shafer theory does not require an assumption regarding the probability of the individual constituents of the set or interval. Combination of evidence in dempstershafer theory technical. Verma is a professor technical safety, atom, stordhaugesund university college, haugesund, norway since march 2012 and has been a professor since feb 2001 and senior hag scale professor since jan 20 with the department ofelectrical engineering at iit bombay with a research focus in reliability and safety engineering he has been on leave from iit bombay since march 2012. Introduction most computer models for engineering applications are developed to help assess a design or regulatory requirement.
Tree analysis in an early design stage using the dempstershafer theory of. Then, the ds evidence theory is employed to combine the individual damage bpas in order to reach the final. Is there any graphical software tool for implementing dempstershafer theory. Software quality and reliability prediction using dempstershafer theory lan guo as software systems are increasingly deployed in mission critical applications, accurate quality and reliability predictions are becoming a necessity. Based on the dempstershafer evidence theory ds evidence theory, the discernment frame is formed, the maximum and minimum values of the network degree and strength are determined. Sep 25, 2015 the grading method for classified protection of information system based on analytic hierarchy process and dempster shafer theory abstract. Semigroup structure of singleton dempstershafer evidence. Fault localization with partially reliable test results using dempstershafer theory abstract. Dempstershafer theory, evidence, uncertainty, expert assessment, event tress analysis, fault tree analysis abstract. Reliability and safety engineering ajit kumar verma.
Representation of confidence in assurance case evidence. This paper examines software defect prediction and aims to improve prediction results using information fusion technique. And then the appealing dempster shafer theory of evidence ds theory is applied to fuse these features, due to the fact that ds theory is better than the classical bayesian approach in handling the information with lack of prior probabilities. Dempstershafer theory is based on the nonclassical idea of mass as opposed to the wellunderstood probabilities of bayes theory. Previous work used a doublytruncated normal distribution or a dempster shafer theory based belief mass to represent confidence in the evidence nodes, but we argue that a beta distribution is more appropriate. The authors present reliability terminology in various engineering fields, viz. This paper starts with the grading standard of classified protection of information system, and comes up with the grading method based on analytic hierarchy process and dempster shafer theory for classified. A new failure mode and effects analysis model using dempster. Previous work used a doublytruncated normal distribution or a dempstershafer theorybased belief mass to represent confidence in the evidence nodes, but we argue that a beta distribution is more appropriate. He combines these methods with techniques capable of dealing with uncertainty bayesian systems, and dempster shafer s evidence theory. Introduction one of the aims of software engineering activities is, cost effective development of high quality software systems 1, that is in the narrowest sense can. Learn about our master of science in software engineering program.
Is there any graphical software tool for implementing dempster. Software quality and reliability prediction using dempster shafer. Software quality and reliability prediction using dempster. We conclude by recapitulating the main results and providing a perspective for future research. Dst can be used when it is not possible to obtain a precise estimation of system response due to the presence of multiple uncertain input parameters. Most accurate prediction models require extensive testing e. A python library for performing calculations in the dempster shafer theory of evidence. Advances in the dempstershafer theory of evidence guide. Information fusion with dempstershafer evidence theory for software. Farmer, phd professor of computer science computer. The bayes set is a special case of the dempster shafer set, where all non. By modeling this unreliability using dempster shafer theory, we managed to.
Dempstershafer theory does not require an assumption regarding the probability of the individual constituents of the set or interval. Information fusion with dempstershafer evidence theory for. The dempster shafer theory of evidence here, ds theory, for brevity, sometimes called evidential reasoning cf. Examples of each of the methods as applied to a simple engineering model will be provided. Based on the dempster shafer evidence theory ds evidence theory, the discernment frame is formed, the maximum and minimum values of the network degree and strength are determined. Vying with the bayes theory is the dempstershafer theory, that deals with measures of belief as opposed to probability. Dec 11, 2018 in this video i will explain you about dempster shafer theory in artificial intelligence and how you have to solve the plaussibility case. Imprecise probability and decision in civil engineering. This is a potentially valuable tool for the evaluation of risk and reliability in engineering applications when it is not possible to obtain a precise measurement from experiments, or when knowledge is obtained. Dempstershafer theory of evidence, and secondorder probability analysis. As a more flexible mathematical tool, dempstershafer theory not only combines with. Exploring the combination of dempstershafer theory and.
He is a university professor and board of governors professor at rutgers university early life and education. Dempstershafer theory and neural network are effective and efficient strategies to. Dempster shafer theory is one of the main tools for reasoning about data obtained from multiple sources, subject to uncertain information. Belief functions base degrees of belief or confidence, or trust for one question on the probabilities for a related question. In practical engineering applications, dempstershafer evidence theory is widely used in multisource information fusion owing to its effectiveness in data fusion. This theory is being released because of following reason. Dst is an evidence theory, it combines all possible outcomes of the problem. The dempstershafer theory of evidence here, ds theory, for brevity, sometimes called evidential reasoning cf.
Shatz, and haiping xu, reasoning under uncertainty for shill detection in online auctions using dempster shafer theory, to appear in international journal of software engineering and knowledge engineering ijseke. Most computer models for engineering applications are. In attempting to adopt dempstershafer theory in engineering practice, the dempstershafer decision theory, i. A novel algorithm for identifying key function nodes in. A python library for performing calculations in the dempstershafer theory of evidence.
Download citation dempster shafer theory in the analysis and design of uncertain engineering systems a methodology for the analysis and design of uncertain engineering systems in the presence. Dempstershafer ds belief theory provides a convenient framework for the development of powerful data fusion engines by allowing for a convenient representation of a wide variety of data imperfections. Then, the ds evidence theory is employed to combine the individual. Red explored the application of the dempster shafer theorem of evidence and developed it to model decisionmaking in intelligence analysis and other assessments. This is a potentially valuable tool for the evaluation of risk and reliability in engineering applications when it is not possible to obtain a precise. Constructing probability boxes and dempstershafer structures. Software quality and reliability prediction using dempstershafer. However the bayesian approach is not without criticisms. And then the appealing dempstershafer theory of evidence ds theory is applied to fuse these features, due to the fact that ds theory is better than the classical bayesian approach in handling the information with lack of prior probabilities.
Dempstershafer theory is one of the main tools for reasoning about data obtained from multiple sources, subject to uncertain information. Shatz, and haiping xu, reasoning under uncertainty for shill detection in online auctions using dempstershafer theory, to appear in international journal of software engineering and knowledge engineering ijseke. The dempstershafer theory of evidence springerlink. Fundamentals of the dempstershafer theory and its applications to system safety and reliability modelling. In this work abstract algebraic properties of the dempster shafer set of mass assignments are investigated and compared with the properties of the bayes set of probabilities. Its capability to assign uncertainty or ignorance to propositions is a powerful tool for dealing with a large range of problems that otherwise would seem intractable. Over the last three decades, bayesian theory has been widely adopted in civil engineering for dealing with uncertainty and for purposes of decision making under uncertainty. In this study, the dempstershafer ds evidence theorybased approach for structural damage detection is presented. Software defect prediction, dempstershafer evidence theory, information fusion, software engineering 1. Shafer, born on november 21, 1946, grew up on a farm. A new methodology, based on a modified dempstershafer ds theory, is proposed for solving multicriteria design optimization problems.
Software defect prediction, dempster shafer evidence theory, information fusion, software engineering 1. As a more flexible mathematical tool, dempstershafer theory not only combines with other mathematical frameworks 1518, but also combines with. Dempstershafer evidence theory approach to structural. This paper starts with the grading standard of classified protection of information system, and comes up with the grading method based on analytic hierarchy process and dempstershafer theory for classified protection of information system. Support for normalized as well as unnormalized belief functions different montecarlo algorithms for combining belief functions various methods related to the generalized bayesian theorem measures of uncertainty methods for constructing belief functions from data. Department of electrical engineering and computer science. The dempstershafer theory of evidence has a systematic theoretical knowledge. Reasoning under uncertainty for shill detection in online auction using dempstershafer theory 3 being an honest bidder. On the use and the performance of software reliability growth. The bayes set is a special case of the dempstershafer set, where all non. A comparative assessment of dempstershafer and bayesian. Dempster shafer ds belief theory provides a convenient framework for the development of powerful data fusion engines by allowing for a convenient representation of a wide variety of data imperfections. Yager r 2019 generalized dempstershafer structures, ieee transactions on fuzzy systems, 27. Dempster 1968 and glenn shafer 1976, but the kind of reasoning the theory uses can be found as far back as the seventeenth century.
International journal of reliability, quality and safety engineeringvol. These activities focus on introduction of human aspects to software systems, and development of more humanaware and humanlike systems. By bridging fuzzy logic and probabilistic reasoning, the theory of belief functions has become a primary tool for knowledge representation and uncertainty reasoning in expert systems. The dempstershafer theory owes its name to work by a.
A new failure mode and effects analysis model using. Theoretical aspects of software engineering tase 2014. The initial work introducing dempstershafer ds theory is found in dempster 1967 and shafer 1976. Dempster shafer theory is a generalization of the bayesian theory of subjective probability. Dempster shafer theory of evidence, belief interval representations for uncertainty, evidence accumulation and propagation 2 lectures knowledgebased decision systems, utility theory, utility functions, decision networks, decisiontheoretic knowledge systems, sequential decision problems, value iteration 3 lectures. In particular, we explored the use of the new techniques to control an autonomous network of marine assets that would be used to monitor a number of marine phenomena. An introduction to bayesian and dempstershafer data fusion. Theoretical aspects of software engineering in 2014, explore presented research, speakers and authors of tase 2014. Special issue on the dempstershafer theory of evidence. By bridging fuzzy logic and probabilistic reasoning, the theory of belief functions has become a primary tool for knowledge representation and uncertainty reasoning in. The current research projects embrace the following areas. Among many theoretical advances, the most noticeable are the theory of linear belief functions,4 the theory of transferable beliefs,5 and the theory of hints.
We developed two novel statistical models based on dempstershafer theory, which provide accurate predictions from relatively small data sets of direct and indirect software reliability and quality predictors. Subsequently, shafer reinterpreted the upper and lower probabilities of the reliability function and created the mathematical theory of evidence. Since each piece of evidence involves uncertainties, it is appropriate to employ some formal reasoning technique 7, 8. Advances in the dempstershafer theory of evidence guide books. Dempster in the context of statistical inference, the theory was later developed by glenn. A methodology for the analysis and design of uncertain engineering systems in the presence of multiple sources of evidence based on dempstershafer theory dst is presented. The dempstershafer theory is a mathematical theory of evidence, which allows one to combine evidences from different sources and arrive at a belief function by taking into account all the available evidences.
Therefore, exploring the combination of dempster shafer theory and neural network can potentially improve the performance and bring new opportunities for trust and distrust prediction. Exploring the combination of dempstershafer theory and neural. Compared with bayesian theory, the dempstershafer theory of evidence feels closer to our human perception and reasoning processes. It outlines the scope of software engineering, including lifecycle models, software process, teams, tools, testing, planning, and estimating. Dempstershafer theory in the analysis and design of. Introduction one of the aims of software engineering activities is, cost effective development of high quality software systems 1, that is in the narrowest sense can be expressed as defect rate of final product 2. Dempster shafer theory ai cse 8th sem linkedin slideshare.
In attempting to adopt dempster shafer theory in engineering practice, the dempster shafer decision theory, i. In this context, we propose to use belief functions in dempster shafer ds theory 9, 10, 11 to model the. Fault localization is a critical procedure in software development process. Reasoning under uncertainty for shill detection in online auction using dempster shafer theory 3 being an honest bidder. A modified dempstershafer theory for multicriteria. The theory of belief functions, also referred to as evidence theory or dempstershafer theory dst, is a general framework for reasoning with uncertainty, with understood connections to other frameworks such as probability, possibility and imprecise probability theories. The dempster shafer theory dst is a mathematical theory of evidence. We proposed a multilevel evaluation index mechanism which is used to classified protection level of information system the. Support for normalized as well as unnormalized belief functions different montecarlo algorithms for combining belief functions various methods related to the generalized bayesian theorem measures of uncertainty methods for constructing belief functions from data both python 2. Keywords fmea, dempstershafer theory, aggregating group responses, incomplete information and uncertainty 1. It is based on dempsters original work dempster, 1967 on the.
Safety case confidence propagation based on dempstershafer. Glenn shafer is an american mathematician and statistician. It can eliminate the uncertainty and ambiguity caused by randomness during the decisionmaking process. Previous studies based their research on the precondition that test results are conveniently acquired and 100% correct, which does not happen in the real world. In this video i will explain you about dempster shafer theory in artificial intelligence and how you have to solve the plaussibility case. Dempstershafer multifeature fusion for pedestrian detection. It is well known that considerable amount of computational information is acquired during the iterative process of optimization. In this paper, we study predicting trust and distrust based on dempster shafer theory and neural network. Results indicate that the prediction results can be improved using dempstershafer evidence theory for information fusion. The grading method for classified protection of information. Fault localization with partially reliable test results using. Fault localization with partially reliable test results. Software quality and reliability prediction using dempstershafer theory lan guo dissertation submitted to the college of engineering and mineral resources at west virginia university in partial ful. Reliability and safety engineering ajit kumar verma springer.
The dempstershafter theory is wellknown for its usefulness to express uncertain judgments of experts. Dempstershafer theory, with an example given of fusion using the cornerstone of the theory known as dempster s rule. Including a tutorial introduction to the dempstershafer theory, the differences between. By modeling this unreliability using dempstershafer theory, we managed to. On the use and the performance of software reliability. The theory came to the attention of ai researchers in the early 1980s, when they were trying to adapt probability theory to expert systems. First, the software complex network model is constructed according to the execution process of the software. Reliability and safety engineering presents an overview of the basic concepts, together with simple and practical illustrations. Toward efficient computation of the dempstershafer belief. Sep 03, 2014 fault localization with partially reliable test results using dempster shafer theory abstract. Information fusion with dempstershafer evidence theory.
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